Abstract
In resource-constrained regions, pneumonia detection is a major challenge which otherwise often proves to be fatal. Since several decades (WHO reports), as chest X-rays provide us visual difference (changes in texture, for example) between normal and abnormal regions, radiologists use them to detect Pneumonia in addition to other sources of data. In this paper, we propose deep neural network that is designed to extract differences in textures from abnormal regions (related to pneumonia). In our experiments, we achieve the highest accuracy of 99.13% using publicly available data: “Chest X-ray Images (Pneumonia)” [13]. The results outperformed handcrafted feature-based tools and other state-of-the-art works.
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Mukherjee, H. et al. (2021). Deep Neural Network for Pneumonia Detection Using Chest X-Rays. In: Singh, S.K., Roy, P., Raman, B., Nagabhushan, P. (eds) Computer Vision and Image Processing. CVIP 2020. Communications in Computer and Information Science, vol 1376. Springer, Singapore. https://doi.org/10.1007/978-981-16-1086-8_8
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DOI: https://doi.org/10.1007/978-981-16-1086-8_8
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